System for pulse oximetry SpO2 determination

ABSTRACT

The improved pulse oximeter preprocesses the sets of red and infrared signals received from the probe to remove ambient light to remove noise and to de-exponentiate the signals. The linearity of the processed red and infrared signals allows the use of statistical techniques such as linear regression and linear correlation to fit a straight line to the set of pairs of processed red and infrared data points and to best measure the goodness of the straight line fit to these data points. The result of this analysis is a linear regression slope and a goodness of fit correlation coefficient. The correlation coefficient is a measure of the linearity of the input data points and if less than a predetermined threshold it indicates that a distorted signal has been received from the probe. This permits the pulse oximeter to detect probe off conditions and/or motion in the patient. The computer linear regression slope is converted to RRatio which is used in an empirical calibration formula to compute the average SpO2 value. The minimum size of the data set required for high confidence calculations using this apparatus is significantly smaller than the a pulse period and permits faster response to changing input data.

RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 08/405,569, filed Mar. 16, 1995, now U.S. Pat. No. 5,934,277,which is a continuation of U.S. patent application Ser. No. 07/753,761,filed Sep. 3, 1991, now abandoned, both of which are hereby incorporatedby reference in their entirety.

FIELD OF THE INVENTION

This invention relates to pulse oximeters which measure the oxygensaturation of arterial hemoglobin and, in particular, to an improvedsystem for performing these calculations and for detecting probe-offconditions.

PROBLEM

It is a problem in the field of pulse oximeters to accurately measurethe oxygen saturation of the hemoglobin in arterial blood without havinga significant error content due to ambient noise. It is also a problemto determine when the probe used to perform the measurement is producingdata with a noise component that is too large to provide an oxygensaturation reading of sufficient accuracy. The oxygen saturation (SpO2)of arterial blood is determined by the relative proportions ofoxygenated hemoglobin and reduced hemoglobin in the arterial blood. Apulse oximeter calculates the SpO2 by measuring the difference in theabsorption spectra of these two forms of hemoglobin. Reduced hemoglobinabsorbs more light in the red band than does oxyhemoglobin whileoxyhemoglobin absorbs more light in the infrared band than does reducedhemoglobin. The pulse oximeter includes a probe that is placed on someappendage which is cutaneous vascular, such as the fingertip. The probecontains two light emitting diodes, each of which emits light at aspecific wavelength, one in the red band and one in the infrared band.The amount of light transmitted through the intervening fingertip ismeasured several hundred times per second at both wavelengths.

The tissue contains arterial, capillary and venous blood as well asmuscle, connective tissue and bone. Therefore the red and infraredsignals received from the probe contain a DC component which isinfluenced by the absorbency of tissue, venous blood, capillary blood,non-pulsatile arterial blood, the intensity of the light source and thesensitivity of the detector. The pulsatile component of the receivedsignals is an indication of the expansion of the arteriolar bed witharterial blood. The amplitude of the pulsatile component is a very smallpercentage of the total signal amplitude and depends on the blood volumeper pulse and the SpO2.

The received red and infrared signals have an exponential relationshipand the respective incident intensities. Therefore, the argument of thereceived red and infrared signals have a linear relationship and thesereceived signals can be filtered and mathematically processed usingeither derivatives or logarithms. The effects of different tissuethicknesses and skin pigmentation can be removed from the receivedsignals by normalizing the processed signal by a term that isproportional to the non-pulsatile portion of the received signalintensity. Taking the ratio of the mathematically processed andnormalized red and infrared signals results in a number which istheoretically a function of only the concentration of oxyhemoglobin andreduced hemoglobin in the arterial blood, provided that theconcentration of dyshemoglobins in the arterial blood is sufficientlysmall.

This data is significantly impacted by the presence of noise, which ismanifested in many forms. Any phenomena, whether mechanical orelectrical or optical, that causes an artifact in the pulsatilecomponent of the received signal compromises instrument performance. Anexample is that any transient change in the distance between the lightemitting diodes and the detector can result in an error signal at bothwavelengths which are of concern in a critical care setting because.These pulse sourses can cause annoying false positive alarms which areof concern in critical care setting because instruments with frequentalarms are often ignored or the alarms are disabled. Motion artifactscan be caused by patient movement and frequently mimic vascular beatswith frequencies well within normal physiological ranges.

A second source of noise is the introduction of ambient light into theprobe. Any light incident on the detector and not originating from thelight emitting diodes is considered noise. Many of these noise sourcesare not as easily filtered out of the resultant signal and represent asignificant error component in existing pulse oximeters.

SOLUTION

The above described problems are solved and a technical advance achievedin the field by the improved system for nonivasively calculating theoxygenation of hemoglobin in arterial blood using a pulse oximeter. Thisimproved system takes advantage of the basic statistical property ofpulse oximetry signals that the measured blood oxygen saturation appearsas a constant over a small set of measurements. Properly processed setsof red and infrared signals should have a linear relationshiptherebetween.

The processing has several steps. First, the received red and infraredsignals are collected from the probe detector and pre-processed. Thispre-processing includes removal of ambient light and prefiltering toremove noise. The pre-filtering can be a combination of linear filteringto remove unwanted additive noise and non-linear filtering to removenoise spikes. The filtered red and infrared signals consist of both asmall magnitude pulsatile component which carries information about theoxygen saturation of the hemoglobin, and a plurality of large magnitudenon-pulsatile components. The filtered signals are mathematicallyprocessed using either derivatives or logarithms. The results of themathematical processing are a set of red values which are directlyproportional to the red optical Extinction, and a set of infrared valueswhich are directly proportional to the infrared optical Extinction. Thered and infrared signals travel through the same pulsatile path when thedata is good, which leads to a technique for both identifying good dataand extracting the ratio of red optical absorption to the infraredoptical absorption for that set of data measurements.

The oxygen saturation is essentially constant for a set of measurementstaken over a short interval of time; e.g., less than a second. Thisimplies that, when the data is good and the optical paths are the samefor the red and infrared signals, the ratio of the mathematicallyprocessed red signal to the mathematically processed infrared signal isalso a constant, except for residual noise. Consequently, for good data,a plot of the simplified infrared data points versus the simplified reddata points yields points that are tightly scattered around a straightline. A “best-fit” straight line can be computed for these data pointsusing standard mathematical techniques such as linear regression. Theslope of that best-fit-line is proportional to the RRatio, which isdefined as the ratio of red optical absorption to the infrared opticalabsorption for that data set. The RRatio carries the desired informationabout oxygen saturation of hemoglobin in the arterial blood.

The processed signals also contain additional information. If theplotted points are widely scattered about the best-fit-line, theexcessive scatter indicates that there is excessive noise or probeartifacts in the received signals. This can be an indication the probehas fallen off the patient and/or there is patient motion. The degree ofscatter of the points can be measured using standard mathematicaltechniques, such as the linear correlation coefficient. The measure ofthe scatter of the plotted points provides a quality measure of the datasample. Other statistical techniques could also be used to check thelinearity of the data; for example, higher-order moments or higher-orderfits. Still another test of data quality can be the intercept of thebest-fit-line. The intercept is very close to zero for good data. Thecomputed error measures can be compared to a failure threshold, possiblyover a series of measurements. If there is too much error, then an alarmcan be generated for the user.

The minimum size of the measured data set required for high confidenceSpO2 calculations is small and therefore permits faster response of thepulse oximeter to changing values of the SpO2. It is not necessary towait a pulse period to acquire enough samples, and high confidencevalues can be computed in less than a second.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 illustrates in block diagram form the overall architecture of theimproved system for SpO2 calculation;

FIG. 2 illustrates in block diagram form the functional elements used bythis system to perform the calculations;

FIGS. 3-7 illustrate additional details of the various processes used inthe improved system for SpO2 calculation;

FIG. 8 illustrates in graphical form the various components of the inputsignal from the probe; and

FIG. 9 illustrates in graphical form an illustrative set of data whichis used in this system.

DETAILED DESCRIPTION

The pulse oximeter system takes advantage of the basic statisticalproperty of pulse oximetry signals that the measured blood oxygensaturation appears as a constant over a small set of measurements.Properly processed sets of red and infrared signals should have a linearrelationship therebetween. Preprocessing of the sets of red and infraredsignals received from the probe removes ambient light components andlinear and non-linear prefiltering also removes noise to provide moreaccurate measurement data.

The filtered signals are mathematically processed using eitherderivatives or logarithms. The results of the mathematical processingare a set of red values which are directly proportional to the redoptical absorption, and a set of infrared values which are directlyproportional to the infrared optical absorption. The presence of gooddata is indicated by a linear relationship between the mathematicallyprocessed red values and the mathematically processed infrared values.

There are several types of appropriate mathematical processing. Forexample, one method is based on a derivative technique which finds thenumerical difference between two successive red measurements. Thisdifference results in a red term that is proportional to the product ofthe red optical absorption and the pulsatile path, provided that thesample rate is rapid enough for the time between measurements to be verysmall. Taking a similar difference between two successive infraredmeasurements provides an infrared term that is proportional to theproduct of the infrared optical absorption and the pulsatile path. Forgood data, the pulsatile path components are identical for both red andinfrared signals, so that the ratio of the differentiated red andinfrared terms cancels out the path length. The effects of differenttissue thicknesses and skin pigmentation are removed by normalizing theprocessed signal by removing the terms that are proportional to the redand infrared non-pulsatile incident intensities. An alternate method ofmathematical processing is to use logarithms instead of derivatives. Inthis approach, the logarithms of the ratio of two successive redmeasurements are taken, which is equivalent to finding the differencebetween the logarithms of two successive red measurements. The sameprocess is applied to the infrared signals. Then the ratio of thelogarithmic differences of the red and infrared signals yields a termthat is a function of the red and infrared absorptions and isindependent of path length. This technique directly compensates for thevariations in thickness of different tissues and skin pigmentation.

Regardless of whether the derivatives or logarithms are used formathematical processing, the linearity of the mathematically processedred and infrared signals allows the use of statistical techniques suchas linear regression and linear correlation to fit a straight line tothe set of pairs of processed red and infrared data points and tomeasure the goodness of that fit. One result of this analysis is alinear regression slope and intercept and the other result is agoodness-of-fit correlation coefficient. The linear regression slope isa measure of the best fit straight line to the ratio of the redabsorption coefficient and infrared absorption coefficient. This ratiois called a best-fit RRatio. This best-fit RRatio is then used in anempirical calibration formula to compute the best-fit SpO2 value. Theminimum size of the data set required for high confidence SpO2calculations is relatively small, and permits faster response of thepulse oximeter system to changing values of SpO2. It is not necessary tohave a full pulse period, and responses under one second are feasible.

The goodness of fit correlation coefficient is a measure of thelinearity between the mathematically processed red and infrared signals.If the correlation coefficient is low, then the data is a poor fit. Thiscan indicate that there is a a probe-off condition or or patient motion.By tracking a short history of computed correlation coefficients againstpre-determined failure thresholds error conditions can be detected andappropriate alarms generated for the user.

System Architecture

FIG. 1 illustrates in block diagram form the overall architecture of atypical pulse oximeter system including the apparatus of the presentinvention. The pulse oximeter system 100 consists of a probe 101connected to probe interface circuit 102 by means of a set of electricalconductors 103. The probe 101 consists of an exterior housing 104 thatapplies the active elements of the probe to the tissue under test, suchas a finger 105, containing an arterial blood flow that is to bemonitored. Included within housing 104 is a pair of light emittingdiodes 111, 112 and at least one corresponding light detector 113. Thelight emitting diodes 111, 112 each produce an output light beam ofpredetermined wavelength which is directed at the tissue under test 105enclosed by housing 104. The light detector 113 monitors the level oflight that is transmitted through the or reflected from the vascularizedtissue 105. In order to distinguish between the light beam produced bythe red 111 and infrared 112 light emitting diodes, these light emittingdiodes 111, 112 are synchronously sampled. Ambient light is measured inhousing by 104 by light detector 113 making measurements when thelighting emitting diodes 111, 112, are off.

The signals received by probe interface circuit 102 from light detector113 are analog signals and are typically processed by additional analogcircuitry and then converted by an analog-to-digital converter circuitinto sets of digital measurements which are stored in memory 106.

Data processing circuit 107 mathematically processes the digitizedmeasurements stored in memory 106 to compute the oxygenation level ofthe hemoglobin in the arterial blood in tissue 105. It is also possiblethat analog circuitry can be used to perform some of the mathematicaloperations described herein as performed by data processing circuit 107,such as taking derivatives or logarithms.

The data processing circuit 107 also computes the pulse rate, and apulsitility index. The results of the oxygen saturation computation andpulse rate are displayed numerically (115) via display driver 109 andthe associated display 114, 115 while the plethysmographic waveform istypically displayed graphically via display 114. The pulsitility indexcan be done with numerical or graphical methods. In addition, dataprocessing circuit 107 detects anomalies in the input data stored inmemory 106 and sets error codes which may be used to activate a alarmgeneration circuit 108 to produce an audible and/or visual alarm to auser to indicate a data error condition. The operation of dataprocessing circuit 107 is disclosed in additional detail below and, forthe purpose of this disclosure, it is assumed that the other elementsdisclosed in FIG. 1 are the conventional components found in existingpulse oximeter systems.

Signal Components

FIG. 8 illustrates in graphical form (not to scale) the variouscomponents of the signal produced by the light detector 113 as a resultof a light beam interacting with vascularized tissue 105. The lightdetector output signal consists of a large magnitude non-pulsatilecomponent and a small magnitude pulsatile component. The non-pulsatilecomponent represents light remaining after absorption due to acombination of venous blood flow, tissue, bone, and constant arterialblood flow while the small pulsatile component is caused by the lightabsorption due to pulsatile arterial blood flow that is to be measured.Since the light emitting diodes 111, 112 are sampled in rapidsuccession, the data signals produced by the light detector 113 andtransmitted to the probe interface 102 consist of a series of datapoints which are digitized and stored in memory 106. Since the red 111and infrared 112 light emitting diodes are sampled in rapid insuccession, the digitized data points produced consist of a plurality ofsets of measurements, with one set corresponding to samples of the redintensity, the other set corresponding to samples of the infraredintensity, and a third set corresponding to ambient light. Ideally, theratio of the normalized derivative (or logarithm) of the red intensityto the normalized derivative (or logarithm) of the infrared intensity isa constant. This constant is indicative of the partial oxygenation ofthe hemoglobin in the arterial blood flow. It is obvious that this ratiochanges as SpO2 changes but, for a short interval with rapid enoughsampling rate, the ratio remains constant.

As noted above, the actual data received by the probe interface circuit102 can include a fairly significant noise component which is caused bya number of sources including motion of the finger 105, the introductionof ambient light into the housing 101, and various sources of electricalnoise. These noise components skew the values of either or both of themagnitudes measured in each set of data points destroying the correctrelationship between the red and infrared signals. Existing pulseoximeter circuits make use of various filtering techniques to minimizethe impact of the noise on the SpO2 value measured by the system.However, a severe limitation of existing filtering circuits is that novery little indication is provided to the user of the severity of thenoise introduced into the measurements. The accuracy of the readingsproduced by a typical pulse oximeter system can be severely compromisedby the probe 101 being improperly affixed to the patient's finger 105without the user being able to detect this condition. In addition, onlya single pass filtering technique is typically used to remove only themost egregious instances of noise in the system. The apparatus of thepresent invention overcomes the limitations of existing pulse oximetersystems by processing the received data in a manner that detectsexcessive noise introduction in the system and provides improved noiserejection in determining the SpO2 measured by probe 101.

Mathematical Background

In this system, the key parameter is called the RRatio. This parametermeasures the ratio of the normalized derivative (or logarithm) of redintensity to the normalized derivative (or logarithm) of infraredintensity. For good data, this parameter corresponds to the ratio of thered arterial optical absorption to the infrared arterial opticalabsorption. The significance is the RRatio can be understood byexamining the optical behavior of light as it passes through tissue. Thelight is scattered and absorbed by all the tissues, but the lightpassing through a pulsing artery or arterial bed will see a moving pathlength. The other tissues are unmoving and contribute to the steadynon-pulsatile signal, but not to the time-varying pulsatile signals. Theabsorption of light by arterial blood is assumed to be only a functionof the oxygenation state of the hemoglobin. Other basic assumptions arethat the Red and InfraRed light travels along essentially the sameoptical path, and that the hardware circuits do not introduce any biasinto the signal extraction.

The waveform of the optical signal is determined by changes in the pulselength, L_(p) and the basic optical signal is described by anexponential relationship according to Beer's law. This means thatintensity of Red, I_(RED), and the intensity of and InfraRed, I_(IR),light absorbed by the blood in a pulsing capillary bed can be describedby the following equations:

I _(RED)=Ambient+I _(NPRED)*Exp[−E _(RED) *C*Lp](1)

I _(IR)=Ambient+I _(NPIR)*Exp[−E _(IR) *C*L _(p)]  (2)

where the Red and IR optical absorption is given by the product of theRed Extinction Coefficient, E_(RED) or InfraRed Extinction CoefficientE_(IR), and the hemoglobin concentrations, C. The concentration, C, andpulsatile path length L_(p) are the same for the Red and InfraRed.

I_(NPRED) is the non-pulsatile red intensity of the signal and isproportional to the product of the light emitting diodes intensity withattenuation factors from propagation through non-pulsatile media andsensitivity and gain factors from the detection electronics. I_(NPIR) isthe corresponding non-pulsatile infrared intensity.

The pulsatile exponential function puts an amplitude modulation on thenon-pulsatile intensity. The equation for the non-pulsatile intensityI_(NP), has several terms.

I _(NP) =Io*A*Exp[−Σ(Ex*Cx*Lx)]  (3)

The term Io is the incident intensity. A is a composite scale factorcaused by scattering losses, losses in detection, and gain in detectioncircuits. There is a composite exponential absorption as the lightpropagates through non-pulsatile intervening tissue, which can bedescribed by a summation of the absorptions by each intervening tissuezone.

The objective of the following mathematical manipulation is to obtainthe ratio of the Extinction coefficients of the Red, E_(Red), and theInfraRed, E_(IR), signals. This ratio is used with a calibrationequation to determine how well the blood is oxygenated. It is assumedthat the ambient light is perfectly subtracted from the receivedsignals.

It is necessary to compute the Extinction coefficients from theexponentials. This can be done with the use of either logarithms orderivatives. The preferred method disclosed herein uses derivatives. Ifthe samples are closely spaced relative to the changes in pulsatile pathlength, L_(p), then a series expansion of the exponential function canbe used. This means that the exponential really looks like a constantplus a term that is linearly proportional to the path length. In otherwords, only the first two terms of the binomial expansion need to beretained. Assume that the difference between two closely spaced in timenon-pulsatile intensities is zero. Taking samples at time t2 and timet1, the derivative of the red intensity can be approximated as thedifference between the two samples:

DRED=I _(NPRED)*(Exp[−E _(RED) *C*L _(p)(t 2)−Exp[−E _(RED) *C*L _(p)(t1)]≈I _(NPRED)* ([1−E _(RED) *C*L _(p)(t ₂)]−[1−E _(RED) *C*L _(p)(t₁)]=I _(NPRED)*(E _(RED) *C*[L _(p)(t ₂)−L _(p)(t ₁)])  (4)

In equation 4 L_(p)(t2) is the pulsatile path length at time 2 andL_(p)(t1) is the pulsatile path length at time 2 and L_(p)(t1) is thepulsatile path length at time 1.

The derivative of the infrared intensity can be approximated in asimilar manner.

DIR=I _(NPIR)*(Exp]−E _(IR) *C*L _(p)(t 2[−Exp[−E _(IR) *C*L _(p)(t1)])≈I _(NPIR)* ([1−E _(IR) *C*L _(p)(t2 )]−[1−E _(IR) *C*L _(p)(t 1)]=I_(NPIR)*(E _(IR) *C*[L _(p)(t 2)−L _(p)(t 1)]  (5)

Note that both DRed and DIR are directly proportional to the Extinctioncoefficients as long as I_(NPRED) and I_(NPIR) are constant over thatsampling interval. Note also that the term C*[L_(p)(t2)−L_(p)(t1)] iscommon to both DRed and DIR. This means that the ratio of DRed and DIRis independent of the concentration and path length changes.

DRED/DIR=(I _(NPRED) /I _(NPIR))*(E _(RED) /E _(IR))  (6)

Now the factor which is due to the ratio of received non-pulsatileintensities can be removed in several ways. In one method, analoghardware is used to separate the received intensities, by finding thelow-pass DC values of the Red and Infrared.

(I _(NPRED) /I _(NPIR))=(I _(NPRED DC) /I _(NPIRDC))  (7)

In the preferred method, the sum of the two or more time samples is used

I _(NPRED) =I _(NPRED(t2)) +I _(NPRED(t1))+  (8)

I _(NPIR) =I _(NPIR(t2)) =I _(NPIR(t1))+  (9)

An instantaneous RRatio is found by multiplying the ratio of derivativesto the ratio of the incident intensities.

instantaneous RRatio=DRED/DIR*(I _(NPIR) /I _(NPRed))=(E _(RED) /E_(IR))  (10)

Path Length. In other words, only the first two terms of the binomialexpansion need to be retained. Define the difference in path lengthbetween samples at time 2 (t₂) and the time 1 (t₁) as D_(L).

D _(L)=(L _(pp)(t ₂)−L _(pp)(t ₁))  (5)

Retaining only the first two terms of the Binomial Expansion of theExponential, the difference between two closely spaced samples taken attime 2 and time 1 will be directly proportional to D_(L).

D _(R) =R _(AC)(t ₂)−R _(AC)(t ₁)≈R _(DC) *R _(EC) *C*D _(L)  (6)

D _(IR) =IR _(AC)(t ₂)−IR _(AC)(t ₁)≈IR _(DC) IR _(EC) *C*D _(L)  (7)

The pathlength change, D_(L), and the concentration, C, are the same forboth the Red and InfraRed signals provided they are closely spaced. Bytaking the ratio of Equation (6) to Equation (7), the common terms canbe removed. This results in the desired ratio of Extinction Coefficientsmultiplied by the a DC Ratio.

D _(R) /D _(IR)=(R _(DC) /IR _(DC))*(R _(EC) /IR _(EC))  (8)

Key Equations

The instantaneous RRatio is a number computed from only two measurementpairs of red and infrared values, which can be corrupted by noise. Thereis another way to compute an RRatio that forms a best-fit to a largernumber of measurements pairs and reduces the sensitivity to noise. Noteonce again that the Extinction Coefficients and incident intensities arenearly constant provided that the sample rate is fast compared to anyphysiological changes. This means that valid data must obey the equationof a straight line. Consequently for a small set of closely spacedsamples, it must be true that

DRED*I _(NPIR)=SlopeA*(DIR*I _(NPRed))+noise  (11

and where

SlopeA=E _(RED) /E _(IR)  (12)

An alternate way to express the linear relationship may be moreapplicable in some cases.

DRed=SlopeB*DIR+noise,  (13)

and where

SlopeB=(I _(NPRed) /I _(NPIR))*(E _(RED) /E _(IR))  (14)

Equations 11 and 12 state that each value of DIR should be scaled bymultiplying by I_(NPIR) and that each value of DRed should be scaled bymultiplying by I_(NPRed). Then a linear regression of all the scaledpoints in the set yields a line whose slope is the best fit to the ratioof the Red Extinction to the Infrared Extinction which is the desiredRRatio.

best-fit RRatio=SlopeA  (15)

If the incident intensities are constant over the data set, then it isnot necessary to scale each value before doing the linear regression.That case corresponds to Equations 13 and 14, which indicate that thelinear regression slope can be computed on DIR and DRed and thennormalized by the ratio of the incident intensities.

best-fit RRatio=SlopeB/(I _(NPRed) /I _(NPIR))  (16)

After finding the best-fit RRatio, the SpO2 is computed from anempirical calibration formula. Equations 11 to 16 are the key equations.They state that there is a linear relationship between the DIR and DRedprovided that the data is good. Consequently, the slope of the linearregression fit to a set of data samples yields a best-fit estimate forthe ratio of the Extinction Coefficients, RRatio. The goodness of thefit can then be used as a test for checking the quality of the data. Anydeviation from the linearity relationship indicates a reduced quality ofthe data.

For completeness, we show how the linear regression method can be usedwhen the red and infrared signals are processed with logarithms insteadof derivatives. In this case, define the difference between thelogarithms of two subsequent red and infrared measurements.

D Log Red=(Log[I _(NPRed) {t 2)]−Log[I _(NPRed)( t 1)])+E _(RED) *C*(L_(p(t2)]) −L _(p(t1)))  (17)

and

D Log IR=(log[I _(NPIR) {t 2)]−Log[I _(NPIR)(t 1)])+E _(IR) *C*(L_(p{t2)) ]−L _(p(t1)))  (18)

If two samples are closely spaced in time, the difference between twonon-pulsatile intensities goes to zero. Then

D Log Red/D Log IR=E _(Red) /E _(IR)  (19)

Since the Extinctions are constant, this means that

D Log IR=SlopeA*D Log Red+noise  (20)

where

SlopeA=E _(RED) /E _(IR)  (12)

Equation 20 and Equation 11 have the same linear form. The results fromequation 20 and 11 differ only if there are significant variations inincident intensity or large amounts of additive noise, or the samplespacing is too large for the series expansion to be valid.

System Implementation

FIG. 2 illustrates in block diagram form the various components usedwithin the data processing circuit 107 of the pulse oximetry system ofthe present invention in order to provide improved data filtering anddata acquisition. The analog data signals received from probe 101 arefiltered by analog hardware and digitized by an analog to digitalconverter in probe interface 102 prior to application to preprocessinput data from probe circuit 201. The preprocessing circuit 201includes prefilter circuit 401 (FIG. 3) which uses digital filteringtechniques for noise removal, frequency selection and data rate control.Nonlinear digital filters can be used in the prefilter circuit 301 ofpreprocessing circuit 201 to reduce the influence of input data that arestatistically beyond the range of the other data values acquired byprobe 101. These nonlinear digital filters can be implemented forexample by median filters, trimmed mean filters and morphologicalfilters.

The input data from probe 101 is typically decomposed into itsnon-pulsatile and pulsatile sub-elements in probe interface 102 in orderto provide accurate measurements of these components. The pulsatilecomponent typically represents 0.5% of the total input signal and thedecomposition of the input signal into pulsatile and non-pulsatilecomponents permits accurate analog to digital conversion of each ofthese components. Therefore, once these two sets of components have beenfiltered by the prefilter circuit 301 of preprocessing circuit 201, theyare applied to reconstruct red, IR and ambient circuit 302 where thecomponents are used to return the acquired input signal back to itsoriginal form. The reconstructed infrared and red data signals are indigital form and the signal reconstructed within reconstruction circuit302 is used for pulse computations and display 115. The red and/orinfrared signals are further processed for waveform display 114 on thepulse oximeter system.

For some hardware configurations the reconstructed red and infraredsignals are transmitted to process ambient input data circuit 202 whichis comprised of the elements illustrated in FIG. 4. The ambient signalis removed from the red and infrared signals in subtract ambient circuit401. In other hardware configurations, the ambient light is removed fromthe red and infrared signals prior to the analog-to-digital converter.In those configurations, the preprocessed input data can be directlysent to process red and infrared component circuit 203. The resultantsignal is then transmitted to process red and infrared component circuit203. The measured, the ambient components are also transmitted toambient analysis circuit 402 where the characteristics of the ambientsignal are used to determine whether there is a probe off condition orexcessive noise in the input signal. This ambient analysis is forwardedto data quality analysis circuit 208 which performs the errordetermination function for the pulse oximeter circuit to identify anyaberrant conditions in the input signals which question the validity ofthe measurements taken as described below.

The red and infrared pulsatile components with ambient componentsremoved circuit 401 are applied to process red and infrared componentcircuit 203. The processing of the red and infrared components consistsof several possible implementations illustrated in FIG. 5. Oneimplementation adaptively differentiates 501 the red and infraredsignals to perform a digital differentiation of these two signals tode-exponentiate the pulsatile component. Adaptive averaging circuit 502performs a local average of the red and infrared signals to estimate thenon-pulsatile component. Alternatively, the differentiation can bereplaced by logarithmic difference circuit 503 which performs a digitalsubtraction of the logarithms of two sequential red signals and the samefor two sequential infrared signals. The logarithmic differences areused in place of the differential and local average values computed bycircuits 501 and 502 respectively. As a result of these computations, aplurality of sets of “processed ” red and infrared data values arestored in memory of data processing circuit 107 wherein each setconsists of the “processed” of the measured red and infrared data valuescomputed at two successive sampling intervals. Successive sets of datavalues therefore represent successive processed samples of measurementstaken in successive time intervals.

Whichever ones of these processes are selected, the output processedsignals are applied to compute pulsatility index circuit 204 whichcomputes a “pulsatility index” indicative of the perfusion of thehemoglobin using a weighted combination of the red and infraredprocessed values. The perfusion is a term used to indicate the volume ofarterial blood flow for a given volume of tissue and by looking at thered and infrared signals separately, an estimation of the present bloodvolume change in the tissue can be determined. The “pulsatility index”is therefore only a figure that can be correlated to the true perfusionof the tissue.

These same processed signals output by process component circuit 203 areapplied to regression computation circuit 205 which is the first stepused in calculating the partial oxygenation SpO2 of the hemoglobin. Theregression computation circuit 205 consists at least one of theplurality of subcircuits illustrated in FIG. 6 and 7. These circuitsillustrate various combinations of regression computation andnormalization. Circuit 601 is typically used to normalize thedifferentials received from process component circuit 203. Thenormalization circuit 601 multiplies each red differential by thecorresponding infrared non-pulsatile intensity and multiplies eachinfrared differential by the corresponding red non-pulsatile intensity.The normalized differentials are then applied to regression circuit 602which performs a linear regression fit using the normalized infrareddifferential as the independent variable and the normalized reddifferential as the dependent variable. The slope of the best fit line(FIG. 9) is a constant value that represents the best fit RRatio forthat data set. A robust regression process can be implemented byremoving the outliers in the collection of data points, or otherstandard methods for robust linear regression.

An alternative method of processing (shown in FIG. 7) performs aregression fit to the differential values that are input to theregression computation circuit 205 and the output of the regressioncircuit is then normalized by normalize slope circuit 603. In eithercase, the output of regression computation circuit 205 is the RRatio forthe data set as well as a set of computation products that are forwardedto compute correlation coefficient circuit 206. In addition, the linearregression can be either a first order polynomial fit or a higher-orderpolynomial fit. In general, good input data should produce a fitted linehaving a finite slope, zero intercept and all higher order terms shouldbe essentially zero.

The compute correlation circuit 206 uses the RRatio calculated byregression computation circuit 205 as well as a set of parameters usedin the computation of the RRatio to determine the goodness of the linearfit of this straight line in the set of red and infrared differentialsthat were transmitted by process component circuit 203 to regressioncomputation circuit 205. A correlation coefficient is calculated whichrepresents the goodness of the linear fit of the straight line to theset of obtained data values. The intercept of the best fit line can bealso computed to to evaluate date quality. The correlation coefficientand zero offset values are transmitted to data quality analysis circuit208 which uses these values as well as the ambient analyses obtainedfrom process ambient input data circuit 202 to verify the quality of theinput data.

Probe-Off Detection

A probe-off condition can be detected by measuring the goodness of thelinear fit. This means that the product of the slope and its inverseshould equal one if the linear fit is a good one.

There is a standard statistical technique for measuring the goodness ofa linear regression fit. The Fisher Linear Correlation essentiallymeasures the goodness of the fit by using the constraint that the slopeand its inverse must a product very close to ±1 if the fit is good. Ifthe product of the slope and its inverse are significantly differentthan ±1, the data is bad. A Higher order polynomial fit can be usedinstead of a linear and then the size of the higher order coefficientsare used as a test.

Data quality analysis circuit 208 monitors the correlation coefficientas well as other indicators to identify excessive input signal noise. Aneural network or knowledge-based system can be used to identify asubset of anamolies indicative of errors. These anamolies can include nsuccessive samples that exceed a predetermined variance and/or excessivevariance in the ambient signal. Furthermore, the SpO2 varies slowly as aresult of physiological changes and excessive correlation of changes inthe determined SpO2 with changes in the ambient is an indication of anerror condition. This circuit may also receive input pulse signal dataavailable from electrocardiograph machines which could be used to checkthe validity of the data and that the pulsatile rate of the data can becompared with the ECG values to identify motion artifacts in the inputdata. By the use of pattern matching, a feature vector can be computedto identify not only the presence of an error condition. It may also beused to classify the error.

The best fit RRatio output by regression computation circuit 205 istransmitted to compute SpO2 circuit 207 where the partial oxygenation ofthe hemoglobin in the arterial blood is computed using an empiricalformula to identify the correspondence between the input data and theactual oxygenation value as noted above. The results of this computationare then applied to display driver 109 for numeric output on display115.

While a specific embodiment of this invention has been disclosed, it isexpected that those skilled in the art can and will design alternateembodiments of this invention that fall within the scope of the appendedclaims.

I claim:
 1. A method for determining oxygen saturation of hemoglobin inarterial blood using signals received from a probe, which signals areindicative of the light absorption of arterial blood, which haspulsatile and non-pulsatile components, at each of a respective one oftwo light wavelengths, said method comprising the steps of: producing,in response to said signals received from said probe, a series of setsof data values, each of said sets including first and second datavalues, which are indicative of the light absorption of arterial bloodat a respective one of said two light wavelengths; storing a pluralityof said sets of first and second data values; computing a ratio of aneffective optical extinction coefficient of said pulsatile component ofsaid arterial blood at a first one of said two light wavelengths to aneffective optical extinction coefficient of said pulsatile component ofsaid arterial blood at a second one of said two light wavelengths, saidratio being determined by regression analysis of n data points, eachdata point corresponding with one of said stored plurality of sets ofdata values, wherein n is a positive integer greater than 2; anddetermining oxygen saturation of said hemoglobin using said ratio. 2.The method of claim 1, wherein said computing step comprises:determining a linear regression fit for said n data points.
 3. Themethod of claim 2, wherein said ratio is determined by a slope of saidlinear regression fit.
 4. The method of claim 2, wherein each of saidfirst data values is determined utilizing at least a different pair ofsuccessive samples of the signals corresponding with said first lightwavelength, and wherein each of said second data values is determinedutilizing at least a different pair of successive samples of the signalscorresponding with said second light wavelength.
 5. The method of claim2, further comprising: utilizing said linear regression fit and said ndata points to obtain a correlation coefficient indicative of a degreeof correlation between said n data points and the linear regression fit;and generating an error indication indicative of the presence of invaliddata values in said plurality of n data points when said correlationcoefficient is outside of a predetermined range.
 6. The method of claim5, further comprising: displaying said error indication to a user ofsaid probe.